@inproceedings{appidi-etal-2020-creation,
title = "Creation of Corpus and analysis in Code-Mixed {K}annada-{E}nglish {T}witter data for Emotion Prediction",
author = "Appidi, Abhinav Reddy and
Srirangam, Vamshi Krishna and
Suhas, Darsi and
Shrivastava, Manish",
editor = "Scott, Donia and
Bel, Nuria and
Zong, Chengqing",
booktitle = "Proceedings of the 28th International Conference on Computational Linguistics",
month = dec,
year = "2020",
address = "Barcelona, Spain (Online)",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2020.coling-main.587",
doi = "10.18653/v1/2020.coling-main.587",
pages = "6703--6709",
abstract = "Emotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data. In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter annotated with their respective {`}Emotion{'} for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30{\%} and 32{\%} respectively.",
}
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<abstract>Emotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data. In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter annotated with their respective ‘Emotion’ for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30% and 32% respectively.</abstract>
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%0 Conference Proceedings
%T Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction
%A Appidi, Abhinav Reddy
%A Srirangam, Vamshi Krishna
%A Suhas, Darsi
%A Shrivastava, Manish
%Y Scott, Donia
%Y Bel, Nuria
%Y Zong, Chengqing
%S Proceedings of the 28th International Conference on Computational Linguistics
%D 2020
%8 December
%I International Committee on Computational Linguistics
%C Barcelona, Spain (Online)
%F appidi-etal-2020-creation
%X Emotion prediction is a critical task in the field of Natural Language Processing (NLP). There has been a significant amount of work done in emotion prediction for resource-rich languages. There has been work done on code-mixed social media corpus but not on emotion prediction of Kannada-English code-mixed Twitter data. In this paper, we analyze the problem of emotion prediction on corpus obtained from code-mixed Kannada-English extracted from Twitter annotated with their respective ‘Emotion’ for each tweet. We experimented with machine learning prediction models using features like Character N-Grams, Word N-Grams, Repetitive characters, and others on SVM and LSTM on our corpus, which resulted in an accuracy of 30% and 32% respectively.
%R 10.18653/v1/2020.coling-main.587
%U https://aclanthology.org/2020.coling-main.587
%U https://doi.org/10.18653/v1/2020.coling-main.587
%P 6703-6709
Markdown (Informal)
[Creation of Corpus and analysis in Code-Mixed Kannada-English Twitter data for Emotion Prediction](https://aclanthology.org/2020.coling-main.587) (Appidi et al., COLING 2020)
ACL